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Handed out introductory slides and gave overview of course, focusing on why vision is an important topic, why computational modeling can be useful, and what makes a particular type of computational model appropriate for a given use.
Note that this class differs from NIP (Neural Information Processing) in being much more qualitative, with very little mathematical work required, and by providing extensive background material on vision. It differs from NC by focusing on large numbers of units organized into topographic maps, rather than on more detailed study of individual neurons. It differs from CCN by being focused on specific results from the neuroscience of vision.
Assigned chapter 1 of the text as background reading.
Began review of biological data about the visual system.
Covered image formation, the gross anatomy of the visual sysem,
the structure and function of the retina, and cell response types
in the retina, LGN, and V1.
Continued review of the visual system, focusing on feature maps
Continued review of the visual system, focusing on lateral interactions, feedback, and higher visual areas.
Assigned chapter 2 of the text as background reading.
Completed review of the visual system, focusing on development.
Suggested background reading: the vision chapter(s) of any
neuroscience textbook, e.g. Bear, Connors, and Paradiso, Neuroscience:
Exploring the Brain, or Kandel, Schwartz, and Jessell,
Principles of Neural Science.
Began review of modeling approaches for computational
neuroscience of vision, focusing on non-developmental models
for early visual areas.
Continued review of modeling approaches, focusing on SOM-based model of learning retinotopy.
Assigned chapter 3 of the text as background reading.
Worked through SOM tutorial on learning mapping
Finished review of SOM-based models,
focusing on how they can be used to illustrate general mapping
principles, despite their limited biological realism.
Introduced the LISSOM model as a more biologically realistic but closely related way to develop maps.
Assigned chapter 4 of the text as background reading.
Introduced the LISSOM model of orientation maps.
Assigned section 5.3 of the text as background reading.
Discussed Assignment 1 (available on the web this
Finished discussion of basic LISSOM model of orientation
Mechanisms for working with large-scale images: contrast-gain control via afferent normalization, and scaling models to larger areas and densities.
Background reading: Chapter 8 starting with
section 8.2.3, and chapter 15 through 15.2.3 (only skimming
Pre-natal and post-natal development of orientation maps.
Background reading: chapter 9.
Discussed Topographica usage and questions about the first assignment.
Discussed what visual features would be useful to measure besides orientation, focusing on properties that can be detected reliably through a small circular aperture with a limited resolution.
Background reading: rest of chapter 5.
Introduced LISSOM models of ocular dominance and joint models
of orientation and ocular dominance.
Models of motion direction and joint ocular dominance,
orientation, and motion direction.
Models of areas beyond V1, and higher-level visual
Background reading: Chapters 16, 17, 18.
Last updated: 2006/03/13 02:32:13
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